Adjoint Transformation Algorithm for Hand-Eye Calibration with Applications in Robotic Assisted Surgery.

Area of Science:

• Robotics
• Computer Vision
• Medical Engineering

Background:

• Hand-eye calibration is crucial for determining robot arm and camera transformations.
• Current methods offer accuracy but face challenges in surgical robotics due to high precision demands and large camera displacements.
• Millimeter-range accuracy is essential for safe and effective surgical procedures.

Purpose of the Study:

• To present a novel iterative method for hand-eye calibration.
• To improve accuracy in surgical robotics applications, particularly with endoscopic cameras.
• To introduce a stereo hand-eye formulation for enhanced calibration.

Main Methods:

• Developed a new iterative hand-eye calibration method using adjoint transformation of twist motions.
• Employed alternating estimations of rotation and translation for convergence.
• Proposed a stereo hand-eye formulation compatible with existing and new methods.

Main Results:

• The proposed iterative method converges to a more accurate solution than closed-form initializations.
• Demonstrated higher calibration accuracy in both synthetic and real-world experiments.
• Experiments with KUKA robot, da Vinci surgical robot, and stereo laparoscope validated the improved accuracy.

Conclusions:

• The novel iterative approach significantly enhances hand-eye calibration accuracy in surgical robotics.
• The proposed stereo hand-eye formulation further contributes to improved calibration precision.
• This work advances the state-of-the-art for precise robotic surgery guidance.